# Multi-Domain Adaptation by Self-Supervised Learning for Speaker Verification

arXiv: https://arxiv.org/abs/2309.14149 · HTML: https://mlsanity.com/paper/2309.14149/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 20 of 20)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [CN-CELEB: a challenging Chinese speaker recognition dataset](https://mlsanity.com/paper/1911.01799/) (2019)
- [Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition](https://mlsanity.com/paper/1906.07317/) (2019)
- [Learning Speaker Representations with Mutual Information](https://mlsanity.com/paper/1812.00271/) (2018)
- [The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA](https://mlsanity.com/paper/1812.10260/) (2018)
- [VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2302.10248/) (2023)
- [Self-Supervised Learning Based Domain Adaptation for Robust Speaker Verification](https://mlsanity.com/paper/2108.13843/) (2021)
- [The 2021 NIST Speaker Recognition Evaluation](https://mlsanity.com/paper/2204.10242/) (2022)
- [Speaker verification using end-to-end adversarial language adaptation](https://mlsanity.com/paper/1811.02331/) (2018)
- [The CORAL++ Algorithm for Unsupervised Domain Adaptation of Speaker Recogntion](https://mlsanity.com/paper/2202.01092/) (2022)
- [Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification](https://mlsanity.com/paper/1811.03063/) (2018)
- [Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning](https://mlsanity.com/paper/2207.05506/) (2022)
- [Adversarial Training for Multi-domain Speaker Recognition](https://mlsanity.com/paper/2011.08623/) (2020)
- [EDITnet: A Lightweight Network for Unsupervised Domain Adaptation in Speaker Verification](https://mlsanity.com/paper/2206.07548/) (2022)
- [Cluster-Guided Unsupervised Domain Adaptation for Deep Speaker Embedding](https://mlsanity.com/paper/2303.15944/) (2023)

## Abstract (arXiv)

In real-world applications, speaker recognition models often face various domain-mismatch challenges, leading to a significant drop in performance. Although numerous domain adaptation techniques have been developed to address this issue, almost all present methods focus on a simple configuration where the model is trained in one domain and deployed in another. However, real-world environments are often complex and may contain multiple domains, making the methods designed for one-to-one adaptation suboptimal. In our paper, we propose a self-supervised learning method to tackle this multi-domain adaptation problem. Building upon the basic self-supervised adaptation algorithm, we designed three strategies to make it suitable for multi-domain adaptation: an in-domain negative sampling strategy, a MoCo-like memory bank scheme, and a CORAL-like distribution alignment. We conducted experiments using VoxCeleb2 as the source domain dataset and CN-Celeb1 as the target multi-domain dataset. Our results demonstrate that our method clearly outperforms the basic self-supervised adaptation method, which simply treats the data of CN-Celeb1 as a single domain. Importantly, the improvement is consistent in nearly all in-domain tests and cross-domain tests, demonstrating the effectiveness of our proposed method.